r2rtf
The clinical-report table engine learned Chinese, then learned to leave RTF entirely
Comparison · Analytics
dfms vs ymlthis
A side-by-side editorial comparison of dfms and ymlthis — release velocity, themes, recent moves, and the top alternatives to consider.
dfms vs ymlthis: at a glance
What is dfms?
Peer-reviewed, feature-complete, and now able to hand its models to other forecasting engines.
dfms estimates dynamic factor models in R, the workhorse for nowcasting economic activity from ragged, mixed-frequency data. The package worked through the Banbura and Modugno (2014) specification in stages — quarterly variables in 0.3.0, AR(1) idiosyncratic errors combined with mixed frequency in 0.4.0 — then declared 1.0.0 feature-complete on completing rOpenSci peer review, adding news decomposition to attribute forecast revisions to specific data releases. Version 1.0.1 adds convert(), which exports fitted models to dlm or KFAS state-space objects.
What is ymlthis?
ymlthis retired itself, naming Quarto as the reason it no longer needs to exist.
ymlthis built R Markdown YAML front matter programmatically — a fluent `yml_*()` interface plus RStudio add-ins, so users did not have to hand-write metadata blocks whose valid fields were scattered across output-format documentation. Version 1.0.0 declares the package retired, with only CRAN-preserving changes to follow, and states the reason plainly: Quarto now provides good YAML support.
dfms vs ymlthis: editorial side-by-side
dfms
0.0ANALYTICS
Peer-reviewed, feature-complete, and now able to hand its models to other forecasting engines.
◆ Current state
dfms estimates dynamic factor models in R, the workhorse for nowcasting economic activity from ragged, mixed-frequency data. The package worked through the Banbura and Modugno (2014) specification in stages — quarterly variables in 0.3.0, AR(1) idiosyncratic errors combined with mixed frequency in 0.4.0 — then declared 1.0.0 feature-complete on completing rOpenSci peer review, adding news decomposition to attribute forecast revisions to specific data releases. Version 1.0.1 adds convert(), which exports fitted models to dlm or KFAS state-space objects.
◆ Where it's heading
The package has finished the implementation programme it set out in its 2023 vignette and is now working on the edges: interoperability with other state-space packages rather than more estimation methods of its own. The convert() function is the clearest signal — instead of implementing smoothing and prediction intervals natively, it hands the model to packages that already have them. The rOpenSci move also puts it on a review-backed, documented footing that research users can cite.
◆ Prediction
Expect continued interoperability and diagnostic work rather than new estimators, since the maintainer has explicitly scoped the package as complete. Bug fixes against RcppArmadillo releases will likely remain the other recurring driver.
ymlthis
0.0ANALYTICS
ymlthis retired itself, naming Quarto as the reason it no longer needs to exist.
◆ Current state
ymlthis built R Markdown YAML front matter programmatically — a fluent `yml_*()` interface plus RStudio add-ins, so users did not have to hand-write metadata blocks whose valid fields were scattered across output-format documentation. Version 1.0.0 declares the package retired, with only CRAN-preserving changes to follow, and states the reason plainly: Quarto now provides good YAML support.
◆ Where it's heading
The retirement is the endpoint of a long drift. Between 2020 and 2022 every release was reactive — patching around a crayon update that mangled rendered YAML, tracking shiny 1.6, following roxygen2 7.0.0, fixing a typo in an add-in. No new capability has landed in six years, and the four-year gap before 1.0.0 had already answered the question the release note finally makes explicit.
◆ Prediction
Nothing further of substance is expected — the stated policy is changes only where CRAN requires them, so the next release, if any, will be a compatibility patch.
Alternatives to dfms and ymlthis
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either dfms or ymlthis.
DoseFinding
New stewardship at openpharma, then two releases adding the methods MCP-Mod was missing
webmockr
The stubbing library added httr2 support, then spent a year cutting itself free of everything else
crul
crul took mocking back from webmockr and made it a property of the client itself
dendroNetwork
Six releases, six identical bodies — the feed carries the package abstract instead of release notes
chattr
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
See all dfms alternatives → · See all ymlthis alternatives →
Recent activity from dfms and ymlthis
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agodfmsconvert() exports models to dlm and KFAS state-space objects
- 5mo agoymlthisymlthis retired; Quarto covers the need ⚡
- 6mo agodfms1.0: rOpenSci review passed, news decomposition added ⚡
- 6mo agodfmsMixed-frequency estimation gains AR(1) idiosyncratic errors
- 9mo agodfmsC++ compatibility with RcppArmadillo 15.0.2
- 1y agodfmsFixes estimation with a single quarterly variable
- 1y agodfmsAdds mixed-frequency estimation via quarterly.vars
- 4y agoymlthisTypo fixed in the miniUI add-in check
- 4y agoymlthisyml_author() accepts yml_blank(); shiny fixes
- 4y agoymlthisciteproc handling moved to newer rmarkdown functions
- 5y agoymlthisPatched a crayon update that mangled rendered YAML
- 5y agoymlthisAdjustments for shiny 1.6
Frequently asked questions
What is the difference between dfms and ymlthis?
They serve adjacent needs but don't currently overlap on shipped themes. dfms and ymlthis are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is dfms better than ymlthis?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dfms and ymlthis are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to dfms?
Top dfms alternatives in Analytics are ranked by recent ship velocity. Browse the "dfms alternatives" section above for the current picks, or visit /alternatives/dfms for the full list with editorial commentary on each.
What are the best alternatives to ymlthis?
Top ymlthis alternatives in Analytics are ranked by recent ship velocity. Browse the "ymlthis alternatives" section above for the current picks, or visit /alternatives/ymlthis for the full list with editorial commentary on each.